Controlling agricultural production areas

ABSTRACT

This disclosure relates to an irrigation system for an agricultural production area. The system receives wide-area meteorological prediction data and sensors deployed within the agricultural production area collect local-area sensor data. A processor stores received data as historical wide-area meteorological prediction data and data from the sensors as historical local-area sensor data. The processor determines a correlation between the historical wide-area meteorological prediction data and the historical local-area sensor data based on the historical wide-area meteorological prediction data and the historical local-area sensor data, and calculates a prediction on water supply relative to water demand within the agricultural production area based on current wide-area meteorological prediction data, and the calculated correlation. The irrigation actuator is then controlled based on the prediction on water supply relative to water demand to define an amount of water to be used for irrigating the agricultural production area.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from Australian ProvisionalPatent Application No 2016904465 filed on 2 Nov. 2016 and AustralianComplete Patent Application No 2017245290 filed on 9 Oct. 2017, thecontent of which is incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to systems and methods for controllingagricultural production areas.

BACKGROUND

Agricultural production is significantly affected by environmentalinfluences. FIG. 1 illustrates an agricultural production 100 wherecrops 101 are grown on a slope of a hill 102. The sun 103 provides lightfor the crops 101 to grow but also causes evapotranspiration which isbalanced by precipitation 104 from clouds 105. However, theprecipitation 104 depends on whether cloud 105 passes hill 102 beforereaching the area above the crops 101, which in turn depends on wind106. In order to compensate for a lack of precipitation, a farmer canmaintain a water reservoir 107 to irrigate the crops 101 when needed.

However, it is often difficult for the farmer to make the best decisionsbecause the multitude of influences makes this decision complicated.Therefore, estimates are often inaccurate, which results in sub-optimalproduction. Therefore, there is a need for a more accurate predictionsuch that farmers can take action on their farms more efficiently.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is not to betaken as an admission that any or all of these matters form part of theprior art base or were common general knowledge in the field relevant tothe present disclosure as it existed before the priority date of eachclaim of this application.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

SUMMARY

An irrigation system for an agricultural production area comprises:

an irrigation actuator;

a receiver for wide-area meteorological prediction data;

a sensor network comprising sensors deployed within the agriculturalproduction area to collect local-area sensor data;

a processor configured to

-   -   store data from the receiver as historical wide-area        meteorological prediction data;    -   store data from the sensor network as historical local-area        sensor data;    -   determine a correlation between the historical wide-area        meteorological prediction data and the historical local-area        sensor data based on the historical wide-area meteorological        prediction data and the historical local-area sensor data;    -   receive current wide-area meteorological prediction data from        the receiver; and    -   calculate a prediction on water supply relative to water demand        within the agricultural production area based on        -   the current wide-area meteorological prediction data, and        -   the correlation between the historical wide-area            meteorological prediction data and the historical local-area            sensor data,            wherein the irrigation actuator is controlled based on the            prediction on water supply relative to water demand to            define an amount of water to be used for irrigating the            agricultural production area.

A method for controlling an agricultural production area comprises:

determining a correlation between historical wide-area meteorologicalprediction data and historical local-area sensor data based onhistorical wide-area meteorological prediction data and historicallocal-area sensor data;

calculating a prediction on a local-area agricultural parameter based on

-   -   current wide-area meteorological prediction data, and    -   the correlation between the historical wide-area meteorological        prediction data and the historical local-area sensor data; and

controlling the agricultural production area based on the prediction onthe agricultural parameter.

It is an advantage that calculating a prediction based on local-areasensor data is more accurate as the calculation captures variations inthe local area that are impossible to incorporate into wide-areaforecasts. For example, a paddock that lies directly adjacent to awaterway has a significantly different microclimate, i.e. more humiddepending on wind direction, than a paddock that is 50 m away from thewaterway. Further, determining the correlation from the sensor data ismore robust and cost efficient than modelling the characteristics of thelocal area. For example, it is difficult and error prone to model thewind across a given terrain. The determined correlation captures theeffect that the wind has to the sensor data. As a result, a wide varietyof micro-climatic effects can be captured without complicated and errorprone modelling. This makes the method readily deployable to any terrainand any type of sensors and wide-area predictions.

Prediction may relate to at least 24 hours into the future.

The historical wide-area meteorological prediction data and thehistorical local-area sensor data may relate to at least 5 days in thepast.

Calculating the prediction on the local-area agricultural parameter maybe based on an agricultural model.

The agricultural model may be based on plant growth.

It is an advantage that considering plant growth makes the result moreaccurate than other models that only rely on soil types, for example, asplant growth can capture different types of plants on the same soiltype.

The agricultural model may comprise a value indicative ofevapotranspiration of plants.

The value indicative of evapotranspiration of plants may be variableover time.

It is an advantage that the calculations can adapt to the current stateof plant growth and therefore ‘track’ the evapotranspiration as theplants grow.

Historical wide-area meteorological prediction data and the currentwide-area meteorological prediction data may comprise wind data anddetermining the correlation and calculating the prediction is based onthe wind data.

The method may further comprise repeatedly updating the correlationbased on further wide-area meteorological prediction data and furtherlocal-area sensor data.

It is an advantage that the method learns over time and gets moreaccurate as more data becomes available.

The agricultural production area may comprise multiple sub-areas, theremay be at least one local-area sensor in each of the multiple sub-areas,and determining the correlation and calculating the prediction may beperformed for each of the sub-areas.

It is an advantage that different sub-areas can be controlledindividually, which enables optimal utilisation across the entire area.This can capture changes of terrain, soil and other influencing factorsacross the different sub-areas.

Calculating a prediction on a local-area agricultural parameter maycomprise calculating a prediction of a plant state and controlling theagricultural production area may be based on the plant state.

The method may further comprise calculating a prediction on futurelocal-area sensor data, wherein controlling the agricultural productionarea may be based on the predicted plant state and the future local-areasensor data.

Controlling the agricultural production area comprises one or more of:

plant;

irrigate;

harvest;

protect; and

feed.

The method may further comprise creating a graphical user interface topresent the prediction on the local-area agricultural parameter to auser.

The method may comprise repeating the step of calculating the predictionfor multiple future times and creating the graphical user interface topresent a time series of the prediction for the multiple future times.

It is an advantage that the user can view the predictions over time andconsider what actions to take. For example, the user may decide to stopirrigation despite a large current water deficit if one of the futurepredictions shows rainfall. In another example the user may need to planstaff utilisation over the next 7 days but if it is predicted to rain,only half the staff may be needed as they will not irrigate on that day,therefore the user will roster staff based on the future prediction ofrainfall.

The graphical user interface may comprise input elements to allow theuser to input planned controlling actions.

It is an advantage that the user can input controlling actions withreference to the predicted values which makes the interface moreintuitive.

The method may further comprise determining a suggestion for controllingthe agricultural production area based on the prediction on theagricultural parameter.

The method may further comprise determining a prediction on the localarea sensor data based on the current wide-area meteorologicalprediction data and the correlation between the historical wide-areameteorological prediction data and the historical local-area sensordata, wherein determining the suggestion may be based on a predefinedrisk associated with local area sensor data where that risk is likely tooccur and the suggestion is determined based on the prediction on thelocal area sensor data to reduce the risk.

The method may further comprise creating a user interface to display thesuggestion.

The local-area agricultural parameter may be a water deficit or watersurplus.

The prediction on the local-area agricultural parameter may comprise aquality parameter indicative of a predicted quality of a produce fromthe agricultural production area and controlling the agriculturalproduction area may comprise optimising the quality parameter.

It is an advantage that the user can directly control the actual outputthat determines the profitability of the value chain. This avoids errorprone and inaccurate guesswork and allows achieving the best qualityresult even when there is a large degree of variation in conditionsacross the farm.

The method may further comprise repeating the step of calculating theprediction on the quality parameter for multiple future times andcreating a graphical user interface to present a time series of theprediction on the quality parameter for the multiple future times.

It is an advantage that the user can directly see at what times in thefuture the quality is optimal.

The quality parameter may comprise an expected shelf life.

Software, when executed by a computer, causes the computer to performthe above method.

A computer system for controlling an agricultural production areacomprises:

a receiver for wide-area meteorological prediction data and local areasensor data;

a processor to

-   -   determine a correlation between historical wide-area        meteorological prediction data and historical local-area sensor        data based on historical wide-area meteorological prediction        data and historical local-area sensor data;    -   calculate a prediction on a local-area agricultural parameter        based on        -   current wide-area meteorological prediction data, and        -   the correlation between the historical wide-area            meteorological prediction data and the historical local-area            sensor data; and

an output port to control the agricultural production area based on theprediction on the agricultural parameter.

Optional features described of any aspect of method, computer readablemedium, computer system or irrigation system, where appropriate,similarly apply to the other aspects also described here.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an agricultural production according to the priorart.

An example will now be described with reference to:

FIG. 2 illustrates a controlled agricultural production.

FIG. 3 illustrates the server from FIG. 2 in more detail.

FIG. 4 illustrates a method for controlling an agricultural productionarea.

FIG. 5 illustrates a database of historical data.

FIG. 6 illustrates an example user interface for one block.

FIG. 7 illustrates an example user interface for multiple blocks.

FIG. 8 illustrates a cumulative user interface.

FIG. 9 illustrates a scatter plot 800 of historical wide-areameteorological prediction data.

FIG. 10 illustrates a scatter plot of predicted wind against measuredwind for a first synoptic condition.

FIG. 11 illustrates a correlation matrix.

DESCRIPTION OF EMBODIMENTS

This disclosure provides a more accurate prediction because thecalculations proposed herein capture variations in the local area thatare practically impossible to incorporate into wide-area forecasts. Thedisclosed method is more robust and cost efficient than modelling thecharacteristics of the local area, such as the wind across a giventerrain. A wide variety of micro-climatic effects can be capturedwithout complicated and error prone modelling.

Within this disclosure and unless stated otherwise, wide-areameteorological prediction data refers to data that is generated by amodel with a limited spatial resolution. For example, wide-area mayrefer to a resolution of 10 km or more, which means that locationswithin a 10 km by 10 km cell have the same prediction. Wide-areameteorological prediction data may also neglect geological features,such as waterways and terrain, below a predefined threshold, such as 100m width of waterways or 100 elevation of terrain or water bodies orrelief features that have a scale less than that of the model grid, orare poorly resolved at the resolution of the model grid.

Wide-area meteorological prediction data may include data calculated byany one or more of the following models:

GFS Global Forecast System (previously AVN)—developed by NOAA

NOGAPS—developed by the US Navy to compare with the GFS

GEM Global Environmental Multiscale Model—developed by theMeteorological Service of Canada (MSC)

IFS developed by the European Centre for Medium-Range Weather Forecasts

UM Unified Model developed by the UK Met Office

GME developed by the German Weather Service, DWD, NWP Global model ofDWD

ARPEGE developed by the French Weather Service, Météo-France

IGCM Intermediate General Circulation Model

WRF The Weather Research and Forecasting model was developedcooperatively by NCEP, NCAR, and the meteorological research community.WRF has several configurations, including:

WRF-NMM The WRF Nonhydrostatic Mesoscale Model is the primary short-termweather forecast model for the U.S., replacing the Eta model.

WRF-ARW Advanced Research WRF developed primarily at the U.S. NationalCenter for Atmospheric Research (NCAR)

NAM The term North American Mesoscale model refers to whatever regionalmodel NCEP operates over the North American domain. NCEP began usingthis designation system in January 2005. Between January 2005 and May2006 the Eta model used this designation. Beginning in May 2006, NCEPbegan to use the WRF-NMM as the operational NAM.

RAMS the Regional Atmospheric Modeling System developed at ColoradoState University for numerical simulations of atmospheric meteorologyand other environmental phenomena on scales from meters to hundreds ofkilometers - now supported in the public domain

MM5 The Fifth Generation Penn State/NCAR Mesoscale Model

ARPS the Advanced Region Prediction System developed at the Universityof Oklahoma is a comprehensive multi-scale nonhydrostatic simulation andprediction system that can be used for regional-scale weather predictionup to the tornado-scale simulation and prediction. Advanced radar dataassimilation for thunderstorm prediction is a key part of the system..

HIRLAM High Resolution Limited Area Model, is developed by the EuropeanNWP research consortia HIRLAM co-funded by 10 European weather services.The meso-scale HIRLAM model is known as HARMONIE and developed incollaboration with Meteo France and ALADIN consortia.

GEM-LAM Global Environmental Multiscale Limited Area Model, the highresolution 2.5 km (1.6 mi) GEM by the Meteorological Service of Canada(MSC)

ALADIN The high-resolution limited-area hydrostatic and non-hydrostaticmodel developed and operated by several European and North Africancountries under the leadership of Météo-France

COSMO The COSMO Model, formerly known as LM, aLMo or LAMI, is alimited-area non-hydrostatic model developed within the framework of theConsortium for Small-Scale Modelling (Germany, Switzerland, Italy,Greece, Poland, Romania, and Russia).

ECMWF European Centre for Medium-Range Weather Forecasts

ACCESS Australian Community Climate and Earth-System Simulator weathermodel by the Australian Bureau of Meteorology

Local-area sensor data means sensor data that is collected at oneparticular point within the agricultural production area. This meansthat the area considered by the local-area sensor data is at least onemagnitude smaller than the area considered by the wide-areameteorological prediction data.

FIG. 2 illustrates the agricultural production 100 from FIG. 1 but nowwith an irrigation system 200 deployed to control the irrigation of theagricultural production 100. The irrigation system 200 comprises amonitoring and control server 201 connected to an irrigation actuator202 and to a receiver 203 for wide-area meteorological prediction data.There is also a sensor network 204 comprising multiple sensors, such asexample sensor 205 deployed within an agricultural production area 206to collect local-area sensor data. The server 201 predicts water supplyrelative to water demand within the agricultural production area 206 andcontrols the actuator 206 accordingly to compensate for any shortfall inwater.

Computer System

FIG. 3 illustrates server 201 in more detail. Server 201 is a computersystem that comprises a processor 302 connected to a program memory 304,a data memory 306, a communication port 308 and a user port 310. Theprogram memory 304 is a non-transitory computer readable medium, such asa hard drive, a solid state disk or CD-ROM. Software, that is, anexecutable program stored on program memory 304 causes the processor 302to perform the method in FIG. 4, that is, processor 302 collectslocal-area sensor data determines a correlation to wide-areameteorological prediction data, such as weather forecast data, predictswater supply relative to water demand within the agricultural productionarea 206 and controls the actuator 206 accordingly to compensate for anyshortfall in water..

The processor 302 may store the calculated water supply relative towater demand or generate a user interface displaying the calculatedwater supply relative to water demand on data store 306, such as HTMLcode on RAM or a processor register. Processor 302 may also send thedetermined values and/or user interface via communication port 308 to awebserver 320 that makes the HTML code available to user 316.

The processor 302 may receive data, such as local area sensor data, widearea meteorological prediction data or user input data, from data memory306 as well as from the communications port 308 and the user port 310,which is connected to a display 312 that shows a visual representation314 of the user interface to a user 316. It is noted that computersystem 201 may be a personal computing system, such as a personalcomputer, smart phone, tablet, phablet or other computing device. Inthose cases, processor 302 and display 312 are part of the same device.In other examples, the data is processed on a server and processor 302generates the user interface in the form of HTML or other web-basedformat. In those cases, the display 312 is part of a different device,such as a personal computing device with an installed web browser orproprietary program application (‘app’) to render the user interfacegenerated by processor 302.

In one example, the processor 302 receives sensor data from sensor 204via communications port 308, such as by using a Wireless Sensor Network(WSN) according to WSN technical standards, including IEEE 802.11—WiFi,IEEE 802.15.4 supporting 6LoWPAN and ZigBee, and LoRaWAN, to support thelocal area networking, and using 3G/4G mobile telecommunications forbackhaul to the processor 302. The WSN may be a decentralised ad-hocnetwork, such that no dedicated management infrastructure, such as arouter, is required or a centralised network with a router or accesspoint managing the network.

In one example, the processor 302 receives and processes the local areasensor data in real time. This means that the processor 302 generates orupdates the user interface every time sensor data is received fromsensor 124 and completes this calculation before the sensor 124 sendsthe next sensor data update. This is an advantage as the wide arearainfall data is often accumulated over 24 hours, which does not allowfor an assessment of shorter time frames, such as 1 hour. In contrast,the local area sensor data from sensor 124 can be captured at rates ofup to or exceeding once per minute, which allows a short-timeassessment. This way, the agricultural production 100 can be controlledin a time frame of hours instead of entire days.

Although communications port 308 and user port 310 are shown as distinctentities, it is to be understood that any kind of data port may be usedto receive data, such as a network connection, a memory interface, a pinof the chip package of processor 302, or logical ports, such as IPsockets or parameters of functions stored on program memory 304 andexecuted by processor 302. These parameters may be stored on data memory306 and may be handled by-value or by-reference, that is, as a pointer,in the source code.

The processor 302 may receive data through all these interfaces, whichincludes memory access of volatile memory, such as cache or RAM, ornon-volatile memory, such as an optical disk drive, hard disk drive,storage server or cloud storage. The computer system 300 may further beimplemented within a cloud computing environment, such as a managedgroup of interconnected servers hosting a dynamic number of virtualmachines.

It is to be understood that any receiving step may be preceded by theprocessor 302 determining or computing the data that is later received.For example, the processor 302 pre-processes sensor data and stores theprocessed sensor data in data memory 306, such as RAM or a processorregister. The processor 302 then requests the sensor data from the datamemory 306, such as by providing a read signal together with a memoryaddress. The data memory 306 provides the data as a voltage signal on aphysical bit line and the processor 302 receives the sensor data via amemory interface.

It is to be understood that throughout this disclosure unless statedotherwise, meteorological prediction, rainfall, variables, sensor dataand the like refer to data structures, including any related metadata,which are physically stored on data memory 306 or processed by processor302. Further, for the sake of brevity when reference is made toparticular variable names, such as “period of time” or “rainfall” thisis to be understood to refer to values of variables stored as physicaldata in computer system 300.

Method for Controlling an Agricultural Production

FIG. 4 illustrates a method 400 as performed by processor 302 forcontrolling an agricultural production area 206. FIG. 4 is to beunderstood as a blueprint for the software program and may beimplemented step-by-step, such that each step in FIG. 4 is representedby a function in a programming language, such as C++ or Java. Theresulting source code is then compiled and stored as computer executableinstructions on program memory 304.

As mentioned above, processor 302 receives 402 wide-area meteorologicalprediction data through receiver 203 and/or data port 308. Themeteorological prediction data may comprise data indicative of thepredicted rainfall in mm over the next 24 hour period of time or windspeed and wind direction in 10 min intervals for the next 24 hours.Receiving the wide-area meteorological prediction data may compriserequesting the data from an web-based interface or from a data fileservice, via FTP, of a meteorology service provider, or may comprisescraping a website of a meteorology service provider. The data files maybe XML files or other formats including axf, grb, dbf, shp, shx, csv,txt, NetCDF.

Processor 302 stores this data from the receiver over time to build adatabase of historical wide-area meteorological prediction data.Processor 302 may store the prediction data for the closest predictiontime. For example, if there is a weather forecast available for each ofthe next five days, processor 302 stores the weather forecast for thefollowing day and repeats this every day. In more detail, on 1 Januaryprocessor 302 stores the weather forecast for 2 January. On 2 Januaryprocessor 302 stores the weather forecast for 3 January and so on. Thisway, processor 302 builds a database of weather forecasts for multipledays. That is, on 31 January processor 302 has created a database of 31entries of historical wide-area meteorological prediction data.

Similarly, processor 302 stores data from the sensor network 204 ashistorical local-area sensor data. In more detail, processor 302 storesthe sensor data for each day. The sensor data may be an aggregate valueof the measurements from sensors 205, such as day average, cumulative,maximum or minimum value. For example, rainfall may be stored ascumulative over 24 hours. Wind may be stored as an average. In theapplication of frost protection processor 302 may store the minimummeasured temperature as the historical local-area sensor data. In oneexample, processor 302 stores the data from each sensor separately. Inanother example, processor 302 calculates an aggregate value acrossmultiple sensors, such as an average, cumulative, maximum or minimumvalue of all sensors. This way, the minimum temperature measured withinthe entire area 206 can be stored, for example.

FIG. 5 illustrates database 500 of historical data comprising a firstrecord 501 and a second record 502. In this example, the historicallocal-area sensor data and the historical wide-area meteorologicalprediction data are stored together in the same record. In particular,first record 501 and second record 502 each comprise data fields forpredicted wind speed 503, predicted wind direction 504, predictedtemperature 505, first sensor wind measurement 506, first sensortemperature measurement 507, second sensor wind measurement 508 andsecond sensor temperature measurement 509.

In this dataset on first day 501 the wind prediction was for 12 km/hfrom the East and a predicted temperature of 20 degrees Celsius. Thefirst sensor measured significantly lower wind speeds (at 506), whichmay indicate that the first sensor is protected from the easterly wind.Unsurprisingly, first sensor also measures a higher than predictedtemperature (at 507). In contrast, second sensor measures higher windthan forecast (at 508) and a lower temperature (at 509) which mayindicate that second sensor is located in a wind funnel for easterlywinds. On the second day 502 the predicted wind changed to southerly andas a result the first sensor measured higher wind speeds (at 506) andthe second sensor (at 508) measured lower wind speeds (at 508) whichindicates that the wind shielding of the sensors is effective forparticular wind directions.

In the above example the measured values directly correspond to thepredicted values, which means both the prediction and the measurementshave wind speed and temperature. It is noted, however, that in otherexamples there is no direct correspondence. For example, the predictionmay be on temperature and the measurement on soil moisture.

As can be seen above, there is a complex interrelationship between thepredictions and the measurements. Returning back to FIG. 4, processor302 determines 402 a correlation between the historical wide-areameteorological prediction data (503, 504, 505) and the historicallocal-area sensor data (506, 507, 508, 509) based on the historicalwide-area meteorological prediction data (503, 504, 505) and thehistorical local-area sensor data (506, 507, 508, 509). This correlationmay be embodied in a variety of forms as will be described in furtherdetail below but may include factors of a linear regression model or ak-means clustering method.

Next, processor 302 receives current wide-area meteorological predictiondata from the receiver. In this context, current prediction data relatesto prediction data that is for a future point in time, that is, theprediction is current at the time of receiving the data. For example, aweather forecast for 2 January received on 1 January is current on 1January and becomes historical on 2 January or 3 January. While theseexamples relate to days, other time periods for forecasting may equallybe used, such as three hours. In one example, prediction relates to atleast 24 hours into the future. In a further example, the historicalwide-area meteorological prediction data and the historical local-areasensor data relates to at least 5 days in the past. This means 5 recordsin the database in the case of daily data or 5*X records for X recordsper day.

Processor 302 now calculates 404 a prediction on water supply relativeto water demand within the agricultural production area 206 based on thecurrent wide-area meteorological prediction data and the correlationbetween the historical wide-area meteorological prediction data and thehistorical local-area sensor data. The prediction on water supplyrelative to water demand may be a prediction on water deficit or watersurplus. For example, the weather forecast for tomorrow is 30 degreeswith 40 km/h of wind. Processor 302 can use the previously calculatedcorrelation to predict a water deficit of 30 mm for tomorrow.

In order to calculate the predicted agricultural parameter, processor302 may first calculate a prediction on the local-area sensor data basedon the correlation and the current wide-area meteorological predictiondata. Processor 302 may then use a predetermined relationship betweenthe local-area sensor data and the agricultural parameter to calculatethe prediction for the agricultural parameter. As described in moredetail below, processor 302 may use an agricultural model or a trainedmachine learning model, such as a regression model, to calculate theagricultural parameter from the local area sensor data. While thecorrelation between the wide-area meteorological prediction data and thelocal-area sensor data is different for each sensor due to localenvironment variations, the relationship between the local-area sensordata and the agricultural parameter may be identical for all sensors orall users of the system. For this reason, more resources can be investedinto the accurate quantification of that relationship and more datacould be available for machine learning of that relationship.

Finally, processor 302 controls the irrigation actuator 202 based on theprediction on water supply relative to water demand to define an amountof water to be used for irrigating the agricultural production area. Forexample, to compensation for a water deficit of 30 mm, processor 302 maydefine a water flow of 100 l/h.

Agricultural Model

In one example, calculating the prediction on the local-areaagricultural parameter in step 404 is based on an agricultural model. Anagricultural model is any method of quantitatively defining anagricultural-specific output. For example, an agricultural model may bea model of foliage growth over time. When seedlings are first planted,the water loss caused by evapotranspiration is minimal but as theseedlings grow evapotranspiration will increase. This means that thewater deficit increases over time for otherwise constant environmentalparameters. Processor 302 considers such effects by using theagricultural model in the prediction. For example, processor 302receives predicted wind data for the next 5 days and predicts the waterdeficit over the next five days considering an increased foliageevapotranspiration for each day.

In another example, the agricultural model comprises plant states. Inthe example of wheat crops, the states may include germination, seedlinggrowth, tillering, stem elongation, booting, head emergence, anthesis(flowering), milk development, dough development and ripening or otherstates of Zadoks decimal growth scale. In the example of a cherry tree,the states may include Dormant, Swollen bud, Bud burst, Early white bud,White bud, Bloom, Petal fall and Fruit set.

The plant progresses through the states depending on the local areasensor data. In particular, the plant progresses through the statesfaster when there is a large amount of sunlight and progresses throughthe states slower when there is a small amount of sunlight. As a result,processor 302 can predict the state of the plants in the future based onthe current wide-area meteorological prediction data and the correlationbetween the historical wide-area meteorological prediction data and thehistorical local-area sensor data. That is, processor 302 selects one ofmultiple possible states based on the predicted local-area sensor data(which is, in turn, based on the wide-area meteorological predictiondata and correlations to the local-area sensor data). The values thatdetermine the state transition may be stored on data store 306 in theform of a state transition matrix or state machine.

For example, each state may be associated with a number of sunlighthours or a value of Watts of irradiation, such as the White bud state ofcherry trees may last for 30 h of local-area sunlight and then change toBloom. As mentioned before, the local-area sunlight may differ betweenblocks since local conditions, such as fog or clouds over mountainranges can significantly affect sunlight values, which is reflected inthe aforementioned correlation. Using the plant states with theassociated values for state transition, processor 302 can predict whenthe plants will be in each plant state.

In one particularly important example, processor 302 can predict basedon the current weather forecast and the correlation to the historicallocal-area sensor data when the plant will be in bloom. The bloom statehas particular characteristics or risks that may also be stored in thestate transition matrix on data store 306. For example, the plant may beparticularly susceptible to pests, mould or other diseases during bloom.If local-area conditions are predicted to exist while the plant ispredicted to be in the bloom state, mitigation measures can be plannedto avoid or reduce the negative impact from these effects. For example,when cherries are not in a bloom state they are not susceptible to frost(low frost risk). However, when they are in a bloom state (high frostrisk) and frost is predicted at the local-area, netting, roofing ormoisture control can be provided to reduce the effect of frost on theflowers. In other words, the prediction on the local-area agriculturalparameter is a prediction on the plant state and the prediction value is‘bloom’. Controlling the agricultural production area then comprisesmitigating the effect of adverse conditions, such as installing roofingor netting or controlling moisture.

The advantage is that netting and roofing and/or other mitigationmeasures can be planned ahead, which is important as it usually takesdays to install those measures.

Updating the Model

In some examples, processor 302 performs the step 402 of determining acalculation repeatedly in order to update the correlation based onfurther wide-area meteorological prediction data and further local-areasensor data. In this sense the proposed system can be fully operationalwithin a few days after deployment with a limited set of data. Overtime, the dataset becomes more complete, which means the predictionsbecome more accurate for a wider range of conditions.

Sub-Areas

In further examples, the agricultural production area 206 comprisesmultiple sub-areas, such as regions, farms, paddocks, rows and evenindividual plants. In particular for the more granular approaches, suchas paddocks, rows and plants, the local-area prediction can addsignificant benefit as the prediction allows the control of eachpaddock, row or plant optimally and independently from the othersub-areas. It is noted that processor 302 performs method 400 for eachof the sub-areas separately. That is, at least one sensor is located ineach sub-area and processor 302 determines the correlation for eachsub-area based on the sensor data from that sub-area. In most examples,the wide-area meteorological prediction data is identical for allsub-areas. In other words, processor 302 determines a first correlationbetween the wide-area meteorological prediction data and the historicalsensor data from a first set of sensors located in a first sub-area.Processor 302 then determines a second correlation between the samewide-area meteorological prediction data and the historical sensor datafrom a second set of sensors located in a second sub-area and so on.This way, the correlation is specific to that particular sub-area andsensor data. In those examples, processor 302 may also control thesub-areas individually based on the calculated prediction specific tothat sub-area. Sub areas may also include protected cropping areas suchas greenhouses, crops under nets, or other forms of protection. Forexample, processor 302 may provide more water to inclined north-facingrows as they receive more intense sunlight than neighbouring rows at adifferent inclination. In another example, the correlation between thewide-area meteorological prediction data and historical local-areasensor data reflects how the conditions inside a greenhouse change fordifferent weather outside, such as how the greenhouse warms up whenthere is sunshine outside. The resulting increase in evapotranspirationand therefore, increase in water demand can be calculated.

Examples of Control

While the above examples relate to irrigation, other means ofcontrolling the agricultural production area can be chosen. For example,the planting of new crops can be optimised to occur at the time whenparticularly beneficial conditions are predicted to exist, such asparticularly high soil temperature. Further, the harvesting can occurbased on a growth and ripening model. That is, processor 302 can predictthe irradiation by the sun over multiple days or months and thereforepredict the time when the crop will be ready for harvesting. Othercontrols relate to the protection of crops for sub-areas that areparticularly prone to hail or damaging winds, for example. Anotherexample is in protected cropping where the crops are covered with a netor other protection mechanism, these can be controlled based onprediction data and historical local data. Further, the feeding ofplants by fertilizer can be controlled based on the prediction of plantstate and other weather constraints to improve the growth performance.

User Interfaces

In further examples, processor 302 also creates a graphical userinterface to present the prediction on the local-area agriculturalparameter to a user. FIG. 6 illustrates an example user interface 600comprising an indication of a plant type 601 and an indication of aselected local area 602, such as a sub-area of agricultural productionarea 206. The values shown in FIG. 6 are calculated for this particularplant type (cherries in this example) and for this particular area(“block 1”) as described above. User interface 600 comprises anindication of today's water deficit 603 as calculated based on theevapotranspiration and rainfall for today. User interface 600 furthercomprises indications of predicted water surplus/deficit for future 3days 611, 5 days 612 and 7 days 613 in the future. It is noted thatthese values 611, 612 and 613 are calculated based on the weatherforecast (i.e. the wide-area meteorological prediction data) for thosefuture times as well as the correlation between the weather forecast andthe sensor data as described above. For example, the 7 days predictedwater deficit is calculated based on the weather forecast for 7 days inthe future and the correlation between the weather forecast and thesensor data.

User interface 600 further comprises an indication of predicted rainfallfrom the weather forecast 621, 622 and 623 for 3 days, 5 days and 7 daysin the future, respectively. User interface 600 also comprises andindication of the predicted evapotranspiration 631, 632, 633 for 3 days,5 days and 7 days in the future, respectively. Controlling theagricultural production area 206 may then comprise the farmer observingthe user interface 600 and instigating control accordingly. Userinterface 600 further provides input elements to allow the farmer toinput control measures that are applied. In particular, user interfacecomprises inputs to provide irrigation amounts 641, 642 and 643 for 3days, 5 days and 7 days in the future, respectively.

FIG. 7 illustrates an example user interface 700 for multiple blocks,that is, for multiple sub-areas of agricultural production area 206. Thesub-areas may have different plant types planted on them, such ascherries or apples. The plant type may define the agricultural modelthat is used to predict the evapotranspiration over time as describedabove.

User interface 700 comprises multiple panels for each of multiplesub-areas, including first panel 701, second panel 711, third panel 721and fourth panel 731. Within the first panel 701 there is an indicationof the plant type 702 and a block or sub-area identifier 703. Panel 701further comprises an indication of a predicted water deficit or surplusfor the next 3 days 704, 5 days 705 and 7 days 706. As described above,the predicted water deficit or surplus is calculated based on theweather forecast for those days and the correlation between the weatherforecast and the sensor data for that particular block. As can be seenin FIG. 7, the values for water deficit/surplus are different for eachblock which illustrates the difference in the correlation between thesensor data from that block and the weather forecast and the differencebetween plant models for plant types. For example, in a second panel 710associated with a second block 713, the plant type 712 is apples and thepredicted 3 day water surplus 714 is significantly different from thepredicted 3 day water deficit 704 for the first block due to differentplant type and different block. In third panel 721, the predicted 3 dayswater deficit 724 is different to the predicted 3 days water deficit 704in the first panel 701 for the first block despite the identical planttype. This illustrates the difference in correlation between the weatherforecast and the local-area sensor data for those blocks.

User interface 700 may further comprise a user input 707, such as aslider or numeric input, that allows the user to set an amount of waterthat is to be added for irrigation of the block. The selected amount ofwater may be shown in mm, which may equate to litres per square meterper day. Processor 302 may have stored on data store 306 the surfacearea of the sub-areas and multiply the surface area with the selectedvalue to calculate the amount of water in litres to arrive at theselected value in mm. For example, for a water deficit of 4 mm, the usermay select 4 mm irrigation to compensate for this deficit. Processor 302may also suggest the calculated water deficit as an irrigation value.Server 201 may control actuator 202 according to the selected amount.

User interface 700 may further comprise an automatic suggestion oncontrolling the agricultural production area. Processor 203 determinesthe suggestion based on the prediction on the agricultural parameter.For example, the agricultural parameter may be the predicted plant stageof the plant under the predicted local area sensor data according to theplant model. As described herein, each plant stage has certain risksassociated with it and the occurrence of the risk event depends on thelocal area conditions as sensed by the sensors.

For example, during fruit growth sunburn is a major risk whichpredominantly occurs when the temperature is high, such as over 30degrees. In this case, processor 203 determines the fruit growth stageas an agricultural parameter and predicts high local-area temperature asdescribed herein. As a result, processor 203 automatically determinesthat shading and/or cooling should reduce sunburn. Accordingly, as shownin FIG. 7, processor 203 includes into the user interface 700 a firstrecommendation 735 to shade and cool the fruit at +3 days, a secondrecommendation 736 to shade and cool the fruit at +5 days and a thirdrecommendation 737 to harvest the fruit at +7 days. The farmer can thenby following the automatic suggestion reduce the risk of sunburnsignificantly, which will increase the output of fruit from theproduction area.

Similarly, processor 203 may suggest some control measures at particulartimes of day. For example, processor 203 predicts that solar irradiationwill decline from midday due to increasing cloud cover and thereforesuggest the application Calcium foliar for the afternoon. It is to beunderstood that in some examples, user interface 700 may show only thesuggestions 735, 736 and 737 and without the predicted data.

Quality Parameters

In one example, the prediction on the local-area agricultural parametercomprises a quality parameter that is indicative of a predicted qualityof a produce from the agricultural production area. For example, theshelf life of lettuce depends on the soil moisture during the 24 hoursbefore harvest. The soil moisture is different for different soil types.For example, sandy soil holds less water as compared to Clay or Loam.Likewise different plants need different soil moisture levels in thesame type of soil to grow optimally. Therefore, the farmer can optimisethe shelf life of lettuce by controlling the farm optimally in the sensethat the harvest is scheduled where optimal soil moisture is predicted.This also means that user interface 600 may comprise an indication ofthe predicted shelf life instead of or in addition to the water surplusdeficit 611, 612, 613. For example, user interfaces 600 and/or 700 mayshow “Medium, Low, Good” for the predicted shelf life of the producewhen harvested in 3 days, 5 days and 7 days, respectively.

Processor 302 may calculate a correlation between the local-area sensordata and the quality parameter. For example, a retailer can feedbackdata indicating the amount of produce that is discarded each day foreach batch of produce. Processor 302 can then look-up the harvest timeand sub-area of that batch and label the record of the local-area sensordata from that sub-area for that harvest time with the amount ofdiscarded produce. Processor 302 can then determine a regression orother learning method to calculate the correlation between local-areasensor data and produce quality. Based on this correlation, processor302 can calculate a predicted quality using the predicted local-areasensor data or the predicted agricultural parameter, such as the waterdeficit. In other examples, the produce quality can be measureddirectly, such as by measuring the sugar content of grapes or by manualtastings.

Future Controls

FIG. 8 illustrates a cumulative user interface 800 comprising a firstchart 801 associated with a first sub-area. First chart 801 comprises atime axis 801 and a water deficit axis 802. Time axis 801 represents theprediction times in the future and user interface 800 comprises a columnfor the cumulative water deficit for each day in the future. Forexample, a first column 811 indicates that on the first day there is apredicted water deficit of 1 mm. On the second day there is also a waterdeficit of 1 mm, which processor 203 adds to the water deficit 811 ofthe first day to calculate a cumulative water deficit of 2 mm asindicated by a second column 812. Equally on the third day and thefourth day the cumulative water deficit rises to 3 mm 813 and 4 mm 814,respectively. In some instances, a farmer would commence irrigation ifit does not rain for more than two days. Using the systems and methodsdisclosed herein and in particular user interface 800, the farmer cansee more accurately what the water deficit is predicted to be in thefuture. In this example, the farmer can set a threshold 820 and can seethat the cumulative water deficit does not reach the threshold before apredicted rain event in 5 days. The predicted rain event reduces thepredicted water deficit which means the farmer can decide not toirrigate without significant negative impact to the farm.

User interface 800 comprises a second chart 851 associated with a secondsub-area. The first chart 801 and the second chart 851 show thepredicted cumulative water deficit over the same time period, whichmeans the underlying weather forecast is the same for the first sub-areaand the second sub-area. However, the correlation to the local-areasensor data is different. As a result, the water deficit in the secondblock surpasses the threshold 820, which indicates that irrigationshould be activated for the second block to improve productivity.Interestingly, irrigation may be activated for any one or more of thefirst, second, third or fourth day to reduce the cumulative waterdeficit on the fourth day. This can be of significant value in caseswhere the water flow for all sub-areas together is restricted to amaximum amount per day, such as a when irrigating from a river. In thatcase, the second block can be irrigated on the first day and a thirdblock can be irrigated on a second day to keep the cumulative waterdeficits of both blocks on the fourth day below the threshold.

Decision Support Tool

As is now apparent from this description, there is a decision supporttool for agriculture provided that delivers agriculture specificparameters from numerical weather prediction model output localisedusing bias correction factors that are developed from in-situobservations and synoptic classification. The decision support tool isdelivered through the UI and UX. Agriculture specific parameters includerate of evapotranspiration (which feeds into irrigation), growing degreedays (which feeds into key events in the growth cycle).

The output of the numerical weather prediction model may be a griddedmodel output of parameters such as 2 m air temperature, 2 m relativehumidity, 10 m wind speed, solar radiation flux, etc. In-situobservations are collected by sensors measuring the same variables, oreasily comparable variables at the point of interest.

Correlations

The following description provides further detail on determining thecorrelation between the historical wide-area meteorological predictiondata and the historical local-area sensor data in order to create localarea predictions. The first example illustrates a synoptic approach andthe second example a machine learning approach using neural networks.

FIG. 9 illustrates a scatter plot 800 of historical wide-areameteorological prediction data comprising a wind speed axis 901, a winddirection axis 902 and a temperature axis 903. Each dot representswide-area meteorological prediction data for one day also referred to asone data point. Processor 203 may cluster the data points resulting in afirst cluster 911 and a second cluster 912, such as by performing ak-means clustering method. In other examples, processor 203 labels eachdata point based on the local area sensor data. For example, processor203 assigns data points to a first cluster if the measured soil moistureis above a threshold and to a second cluster if the measured soilmoisture is below the threshold. In other examples, processor 203clusters the data points based on the local-area agricultural parameter,such as water deficit. For example, processor 203 clusters data pointsusing a threshold of 3 mm water deficit.

Each cluster of data points may represent a synoptic condition. Synopticin meteorology refers to general view of the weather in the region. Forexample, first cluster 911 represents the synoptic condition of a hotday with strong northerly winds. Second cluster 912 represents lightwinds from varying directions and moderate temperatures. Processor 203may then calculate the correlation between the wide-area meteorologicalprediction data and the local-area sensor data for each cluster orsynoptic condition separately. In one example, processor 203 develops aseries of defining synoptic conditions that balance smaller number ofgroups, and hence large data for analysis against a larger number ofgroups but less data in each group for validation and analysis. This isperformed using a hybrid system knowledge / machine learning approachwhere the initial broad classes are determined using environmentalsystem knowledge, the expansion of these classes is evaluated usingautomated ML methodologies. In one example the original framework couldcontain 8 wind directions, 3 wind classes and a season, giving 96discrete synoptic categories for which corrections will be determined inan ongoing and automated fashion for weather variables that are used inagricultural calculations.

Processor 203 accesses a subset of the gridded weather forecast datafrom the Global 0.25 degree (per cell) Forecast Model (GFS), noting thismodel could be replaced by any weather forecasting model. Key weatherforecast parameters, such as wind direction, wind speed, relativehumidity, temperature, surface solar radiation, soil moisture, etc. areused to generate a discrete synoptic situation. In one example,processor 203 takes wind speed and direction, the mean temperature overthe last week and the seasonal averages to determine a synopticsituation. An example day may be a hot windy northerly condition. Forthis synoptic condition past records have shown that the rainfall atsensor site A is normally lower than predicted by the model. Thiscorrection has both a quantity (as a scale factor) and a degree ofcertainty based on the spread of the previous values. This correction isapplied to the weather variable prior to an agricultural parameter beingcalculated. Processor 203 calculates the local weather corrections (andhence the local weather) where there are observations to build thecorrection matrix based on sensors (this can be a weather station withinthe grid).

FIG. 10 illustrates a scatter plot 1000 for the first cluster 911comprising a first axis 1001 for the historical predicted wind speedfrom the meteorological prediction data and a second axis 1002 for themeasured wind speed from the local-area sensor data. As can be seen fromthe data points, there is a strong correlation but the measured windspeed 1002 is about 0.5 times the predicted wind speed as indicated by aregression line 1003. As a result, processor 203 stores “0.5” as thecorrelation between the historical meteorological prediction data andthe historical sensor data. The agricultural model then links thelocal-area wind speed to the water deficit. In this example, theinfluence by the remaining parameters from the meteorological predictiondata, such as temperature, are insignificant and can be neglected. Inother words, when the wind is strong, the water deficit does notsignificantly depend on the temperature. In other examples, therelationship is more complicated. For example, there may be 10parameters measured by the local-area sensors 205 including temperature,relative humidity, wind, rain, leaf wetness, solar irradiance,photosynthetic active radiation, frost detection, soil moisture and soiltemperature. Further, there may be 3 parameters predicted in thewide-area meteorological data including wind, temperature and rainfall.

FIG. 11 illustrates a correlation matrix 1100 including the multipliers,representing the correlation, for calculating the predicted local-areasensor data from the predicted meteorological prediction data. Eachentry in matrix 1100 may be referred to as a bias correction factor andmay be calculated from a classification matrix based on multiple modeloutput variables that when considered together can represent differentsynoptic conditions that have an impact on the model calculations andtheir representation of localised point measurements. In one example,there is one matrix for each of multiple synoptic conditions.

In other examples, processor 302 performs a parameter selectionalgorithm to select the most significant parameters from the data, suchas by performing a principle component analysis.

Using the second approach, the machine learning or neural networkapproach begins by pre-processing the data to get it in a suitableformat and a suitable structure. Processor 302 then calculatescorrelations between different variables to determine what variables aregood predictors. In one example, variables are good predictors whentheir absolute correlation value is higher than 0.7. The good predictorvariables are then included in the model. Processor 302 then creates alist of variables from the raw data to indicate the best parameters forthe model. Processor 302 determines correlations by retrievinghistorical wide-area meteorological prediction data and historicallocal-area sensor data and determining the Pearson's correlationco-efficient for each variable pair. The Pearson's correlationco-efficient is calculated using the formula:

${\rho \; x},{\gamma = {\frac{{E\lbrack{XY}\rbrack} - {{E\lbrack X\rbrack}\; {E\lbrack Y\rbrack}}}{\sqrt{{{E\lbrack X^{2} \rbrack} - \lbrack {E\lbrack X\rbrack} \rbrack^{2}}\mspace{11mu}}\sqrt{{E\lbrack Y^{2} \rbrack} - \lbrack {E\lbrack Y\rbrack} \rbrack^{2}}}.}}$

An initial determination of appropriate machine learning models mayidentify the best candidate. Processor 302 selects one or more machinelearning models by evaluating initial candidates of models on scenariosand identifying the best fit based on coarse initial results.

After processor 302 selects the model with the best fit, the model canbe tuned by changing the number of hidden layers and the number of nodesin each layer. In some examples, processor 302 performs a parameterselection algorithm to select the most significant parameters from thedata by performing a principle component analysis to categorize thevariables.

In one example, the neural network models are used to predict wind speedand direction, relative humidity, temperature, rainfall amount and rainprobability, leaf wetness and soil moisture. However, other combinationof parameters may be used. The tuning of the model may include:

-   -   add min max scaling to the model;    -   apply principle component analysis;    -   build the neural network by adding layers (most models have        between 3 and 4 layers).

Processor 302 tests the model on a training set of data and compares theresults to a test set to determine accuracy. Depending on the results,processor 302 adjusts the model by adding or removing layers andautomatically adjusts the techniques used by the model such as inputweights and optimisation technique.

The input data to the models is a combination of wide-areameteorological prediction data sourced from the Global ForecastingSystem (GFS) at a 0.25 degree grid. The data inputs taken from the GFSinclude weather predictions such as temperature, humidity, pressure,cloud cover (and its various forms), dew point, solar radiation, wind,rainfall rate and total rainfall, predicted sunshine duration,geopotential height at different wind levels, storm motion, surfacegust, convective precipitation, freezing rain categories, ice pelletcategories, snowfall and rainfall categories, ground heat flux, icecover, Haines index, latent heat net flux, evaporation rate, sensibleheat net flux, soil moisture, surface temperature, soil temperature,water run-off and wilting point. The GFS data for the last 12 months isincluded as the input as it provides a years' worth of seasonalvariation. The second input set is actual sensor data at the location ofprediction (i.e. local-area sensor data). The sensor data includesTemperature, humidity, pressure, leaf wetness, soil moisture, PAR, PYR,rain, wind speed and direction. All available sensor data can be used asan input.

To create the predictions Processor 203 evaluates the model over aportion of the historical data (i.e. between 70 and 100%) to train at aregular interval for example, once a day. This means processor 203constantly improves the model and the system can be deployed without anyprior knowledge of the local conditions. After a few days, predictionscalculated by processor 203 will become more accurate than the wide-areameteorological prediction data. Processor 203 then runs the models overthe new data received from the sensors and the GFS at regular intervals(for example every 2-6 hours) to create the latest predictions. Themodels create an hourly prediction for the next 7 days (i.e. 168 hourlypoints) for each variable.

The prediction models in use to predict the 8 growing variables include:

Kernel Neurons/ Optimiser/ initialisa- Layers/ Trees/C Loss tion/MaxActivation/ Input Type Depth Parameter Function Features Order Rule WindPressure, Temperature, Neural 4 30; 40; Adam Normal Relu Speed RelativeHumidity, Wind, Network 80; 168 Rainfall, Dewpoint and Random 6 100  5Information Solar Radiation from Forest Gain GFS; Wind from SensorSupport 100  Hinge Radial Vector basis Machine function Wind Pressure,Temperature, Neural 4 30; 40; Adam Normal Sigmoid Direction RelativeHumidity, Wind, Network 80; 168 Rainfall, Dewpoint and Random 6 100  5Information Solar Radiation from Forest Gain GFS; Wind from SensorSupport 100  Hinge Radial Vector basis Machine function RainfallTemperature, Pressure Neural 4 100, 200, Adam Normal Relu and andRelative Humidity Network 300, 1 Sigmoid from Sensors; Random 4 50 5Gini Index Temperature, Pressure, Forest Relative Humidity, CloudSupport   0.5 Hinge Poly- Cover, Convective Cloud, Vector nomial DewPoint, Wind, Wind Machine Gust, Heat Flux, Haines, Precipitation, U-VStorm from GFS Temperature Surface Temperature Neural 1  1 Adam NormalRelu from sensors and Network Prediction of Random 1 10 5 InformationTemperature from GFS Forest Gain Support   0.1 Hinge None Vector MachineHumidity Pressure, Temperature, Neural 4 20; 40; Adam Normal ReluRelative Humidity, Wind, Network 80; 168 Rainfall, Dewpoint and Support1000  Hinge Radial Solar Radiation from Vector basis GFS; RelativeHumidity Machine function from Sensor Barometric Pressure from GFS andLasso 1 N/A N/A N/A Relu Pressure sensor data Model Random 2 25 12  GiniIndex Forest Support 10 Hinge Radial Vector basis Machine function LeafLeaf Wetness from Neural 4 26; 150; RMSprop Normal Relu WetnessSensors - Temperature, Network 150; 1 Cloud Cover, Convective Random 6100  12  Gini Index Cloud, Dew Point, TCC, Forest Wind, EvaporationRate, Support 100  Hinge Poly- Relative Humidity, Soil Vector nomialMoisture, Sunshine, Machine Haines, Pressure, Rain, Radiation Flux,Surface Temp, Heat Flux, Precipitation from GFS Soil Soil Moisture fromNeural 5 For 4 Adam Normal Relu Moisture sensors; Soil moisture Networklayers: and DewPoint from 1352, 60, Forecast model(GFS) 120, 240, 672For 1 depth: 1352, 60, 120, 168 Random 4 50 2 Gini Index Forest Support10 Hinge Poly- Vector nomial Machine

In one example Leaf wetness may be predicted from a neural network thatlearns the correlations between the wide-area meteorological predictiondata and the sensor data from the farm and is run using Processor 203.In another example Soil Moisture may be predicted using a support vectormachine running on processor 203.

Sensors

In one example, the following sensor components are employed in thesense that any combination of one or more of those components constituteone sensor 205:

Sensor Model Vendor Temperature + Relative Humidity VP-4 Decagon(including radiation shield) Anemometer 6410 Davis Instruments RainGauge 7857M Davis Instruments Leaf Wetness LWS Decagon PyranometerSP-212 Apogee Photosynthetic Active Radiation SQ-212 Apogee FrostDetector − temperature SF-410 Apogee Soil Moisture + Soil TemperatureGS-3 Decagon Soil Moisture + Soil Temperature EP100DL-4 EnviroPro

Example

One example is an agricultural production area operated by an applegrower. The produced apples should meet specific and challengingstandards—75 mm, 175 g, 90% blush, 14.5% sugar and just the rightpressure. The more apples that meet this spec, the higher the profit ofthe apple grower. On a 50 tonne per hectare crop, the profit can bedoubled with a 10% increase in packout—the % of apples that are notrejected—Packout is a direct measurement of overall quality.

The apple season starts with bud burst and the trees should haveaccumulated enough winter chill such that fruit set can occur—800 chillhours may be a target. The first task is to regulate fruit set. If thereare too many flowers that set fruit, the then the apples will be toosmall—if there are not enough they will too big. So thinners need to beapplied to the crop in exactly the right set of weatherconditions—balancing heat units, sunlight hours, soil moisture andproduct selection. The methods and systems disclosed above canautomatically suggest the use of thinners as a control of the productionarea to the farmer via the user interface. The proposed methods andsystems monitor temperature trends during the day and at night. Thisautomated solution avoids mistakes which may lead to having to send increws to hand thin excessively, which could lose the entire profit forthe year. Equally, if the flowers are over thinned at the beginning,there will be little to no crop.

At the same time blossom is occurring, and a major concern is diseaseand pest outbreaks as well as managing shoot growth and root growth. Forexample, one day at 15 C with more than six hours of leaf wetness andblack spot is a risk. If a coddling moth outbreak is detected there is awindow of 110 degree days (every hour over 10 C) before the grub comesout. Then there is a choice of when to respond and with which productand to which part of the orchard. Different products work in differentconditions and have vastly different costs. A mistake here can diminishthe 10% increase in packout. The systems and methods disclosed hereincan predict the parameters at the local area and therefore automaticallysuggest the most appropriate product as a control of the agriculturalproduction area. This reduces the risk to the farmer of losing profit.

The next stage is cell division. This 4 week period sees all the applescells put down—ultimately determining the fruit's size potential. Theapple's firmness is decided here too, if the right amount of water andcalcium is applied, to the right part of the tree, in the correct form,the foundations of crisp fruit have been laid. Calcium foliar has to beapplied thoroughly though—sprayed in the heat of the day may reducepackout by 5%—rejected with fruit burn. Fertiliser also should be keptup at the same time. Again, the systems and methods disclosed herein canpredict the parameters at the local area and therefore automaticallysuggest the most appropriate application regime as a control of theagricultural production area and display the suggestion on the userinterface.

A further risk at this stage is mite outbreak which is devastating forthe colour target. Further, hail, birds and wind can cause damage. Asharvest approaches there may be more time required to achieve the sizetarget as ETo has been high this year and sizing challenging. Butirrigation may not be a solution because the fruit could become soft andbe sensitive to bruising. If the fruit is left on the tree too long itmight get sunburnt (which is the second biggest reason for rejectionafter bruising in Australia) or worst still, picking may be too late,which severely compromises storage potential and shelf life. Again, thesystems and methods disclosed herein can predict the parameters at thelocal area and therefore automatically suggest the most appropriatetiming as a control of the agricultural production area.

Every step of the way the farmer makes critical decisions under timepressure. The methods and systems described herein give growers thetools to make confident decisions every step of the way—reducinguncertainty and reducing risk.

While examples herein relate to controlling agricultural productionareas, the systems and methods disclosed herein may equally beapplicable to other operations, including, but not limited toaquaculture, mining, natural resources, environmental monitoring,logistics, insurance and finance, building and construction and healthIn this sense, there is provided a method for controlling an operationin an operational area. The method comprises determining a correlationbetween historical wide-area meteorological prediction data andhistorical local-area sensor data based on historical wide-areameteorological prediction data and historical local-area sensor data.The method further comprises calculating a prediction on a local-areaoperational parameter based on current wide-area meteorologicalprediction data, and the correlation between the historical wide-areameteorological prediction data and the historical local-area sensordata. The method also comprises controlling the operation based on theprediction on the operational parameter.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the specific embodimentswithout departing from the scope as defined in the claims.

It should be understood that the techniques of the present disclosuremight be implemented using a variety of technologies. For example, themethods described herein may be implemented by a series of computerexecutable instructions residing on a suitable computer readable medium.Suitable computer readable media may include volatile (e.g. RAM) and/ornon-volatile (e.g. ROM, disk) memory, carrier waves and transmissionmedia. Exemplary carrier waves may take the form of electrical,electromagnetic or optical signals conveying digital data steams along alocal network or a publically accessible network such as the internet.

It should also be understood that, unless specifically stated otherwiseas apparent from the following discussion, it is appreciated thatthroughout the description, discussions utilizing terms such as“estimating” or “processing” or “computing” or “calculating”,“optimizing” or “determining” or “displaying” or “maximising” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that processes and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

The present embodiments are, therefore, to be considered in all respectsas illustrative and not restrictive.

1. An irrigation system for an agricultural production area comprising:an irrigation actuator; a receiver for wide-area meteorologicalprediction data; a sensor network comprising sensors deployed within theagricultural production area to collect local-area sensor data; aprocessor configured to store data from the receiver as historicalwide-area meteorological prediction data; store data from the sensornetwork as historical local-area sensor data; determine a correlationbetween the historical wide-area meteorological prediction data and thehistorical local-area sensor data based on the historical wide-areameteorological prediction data and the historical local-area sensordata; receive current wide-area meteorological prediction data from thereceiver; and calculate a prediction on water supply relative to waterdemand within the agricultural production area based on the currentwide-area meteorological prediction data, and the correlation betweenthe historical wide-area meteorological prediction data and thehistorical local-area sensor data, wherein the irrigation actuator iscontrolled based on the prediction on water supply relative to waterdemand to define an amount of water to be used for irrigating theagricultural production area.
 2. A method for controlling anagricultural production area, the method comprising: determining acorrelation between historical wide-area meteorological prediction dataand historical local-area sensor data based on historical wide-areameteorological prediction data and historical local-area sensor data;calculating a prediction on a local-area agricultural parameter based oncurrent wide-area meteorological prediction data, and the correlationbetween the historical wide-area meteorological prediction data and thehistorical local-area sensor data; and controlling the agriculturalproduction area based on the prediction on the agricultural parameter.3. The method of claim 2, wherein prediction relates to at least 24hours into the future.
 4. The method of claim 2 or 3, wherein thehistorical wide-area meteorological prediction data and the historicallocal-area sensor data relates to at least 5 days in the past.
 5. Themethod of claim 2, 3 or 4, wherein calculating the prediction on thelocal-area agricultural parameter is based on an agricultural model. 6.The method of claim 5, wherein the agricultural model is based on plantgrowth.
 7. The method of claim 5 or 6, wherein the agricultural modelcomprises a value indicative of evapotranspiration of plants.
 8. Themethod of claim 7, wherein the value indicative of evapotranspiration ofplants is variable over time.
 9. The method of any one of the claims 2to 8, wherein historical wide-area meteorological prediction data andthe current wide-area meteorological prediction data comprises wind dataand determining the correlation and calculating the prediction is basedon the wind data.
 10. The method of any one of the claims 2 to 9,further comprising repeatedly updating the correlation based on furtherwide-area meteorological prediction data and further local-area sensordata.
 11. The method of any one the claims 2 to 10, wherein theagricultural production area comprises multiple sub-areas, there is atleast one local-area sensor in each of the multiple sub-areas, anddetermining the correlation and calculating the prediction is performedfor each of the sub-areas.
 12. The method of any one of the precedingclaims, wherein calculating a prediction on a local-area agriculturalparameter comprises calculating a prediction of a plant state andcontrolling the agricultural production area is based on the plantstate.
 13. The method of claim 12, further comprising calculating aprediction on future local-area sensor data, wherein controlling theagricultural production area is based on the predicted plant state andthe future local-area sensor data.
 14. The method of any one of theclaims 2 to 13, wherein controlling the agricultural production areacomprises one or more of: plant; irrigate; harvest; protect; and feed.15. The method of any one of the claims 2 to 14, further comprisingcreating a graphical user interface to present the prediction on thelocal-area agricultural parameter to a user.
 16. The method of claim 15,wherein the method comprises repeating the step of calculating theprediction for multiple future times and creating the graphical userinterface to present a time series of the prediction for the multiplefuture times.
 17. The method of claim 15 or 16, wherein the graphicaluser interface comprises input elements to allow the user to inputplanned controlling actions.
 18. The method of any one of claims 2 to17, further comprising determining a suggestion for controlling theagricultural production area based on the prediction on the agriculturalparameter.
 19. The method of claim 18, further comprising determining aprediction on the local area sensor data based on the current wide-areameteorological prediction data and the correlation between thehistorical wide-area meteorological prediction data and the historicallocal-area sensor data, wherein determining the suggestion is based on apredefined risk associated with local area sensor data where that riskis likely to occur and the suggestion is determined based on theprediction on the local area sensor data to reduce the risk.
 20. Themethod of claim 18 or 19, further comprising creating a user interfaceto display the suggestion.
 21. The method of any one of claims 2 to 20,wherein the local-area agricultural parameter is a water deficit orwater surplus.
 22. The method of any one of claims 2 to 21 wherein theprediction on the local-area agricultural parameter comprises a qualityparameter indicative of a predicted quality of a produce from theagricultural production area and controlling the agricultural productionarea comprises optimising the quality parameter.
 23. The method of claim22, further comprising repeating the step of calculating the predictionon the quality parameter for multiple future times and creating agraphical user interface to present a time series of the prediction onthe quality parameter for the multiple future times.
 24. The method ofclaim 22 or 23, wherein the quality parameter comprises an expectedshelf life.
 25. Software that, when executed by a computer, causes thecomputer to perform the method of any one of claims 2 to
 24. 26. Acomputer system for controlling an agricultural production areacomprising: a receiver for wide-area meteorological prediction data andlocal area sensor data; a processor to determine a correlation betweenhistorical wide-area meteorological prediction data and historicallocal-area sensor data based on historical wide-area meteorologicalprediction data and historical local-area sensor data; calculate aprediction on a local-area agricultural parameter based on currentwide-area meteorological prediction data, and the correlation betweenthe historical wide-area meteorological prediction data and thehistorical local-area sensor data; and an output port to control theagricultural production area based on the prediction on the agriculturalparameter.